Abstract

The paper is focused on the implementation of a modular color image difference model, as described in [1], with aim to predict visual magnitudes between pairs of uncompressed images and images compressed using lossy JPEG and JPEG
2000. The work involved programming each pre-processing step, processing each image file and deriving the error map,
which was further reduced to a single metric. Three contrast sensitivity function implementations were tested; a
Laplacian filter was implemented for spatial localization and the contrast masked-based local contrast enhancement
method, suggested by Moroney, was used for local contrast detection. The error map was derived using the CIEDE2000
color difference formula on a pixel-by-pixel basis. A final single value was obtained by calculating the median value of
the error map. This metric was finally tested against relative quality differences between original and compressed
images, derived from psychophysical investigations on the same dataset. The outcomes revealed a grouping of images
which was attributed to correlations between the busyness of the test scenes (defined as image property indicating the
presence or absence of high frequencies) and different clustered results. In conclusion, a method for accounting for the
amount of detail in test is required for a more accurate prediction of image quality.